Signal characteristic adjustment apparatus and signal characteristic adjustment method

ABSTRACT

A coefficient calculation unit calculates a coefficient (p) according to input signals (x[n]).A balance setting unit decides, according to this coefficient (p), a coefficient used to control the level of a signal in a digital level correction unit and a coefficient used to adjust the characteristics of a signal in a characteristic correction unit, thus allowing the digital level correction unit and characteristic correction unit to execute adaptive processing

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromprior Japanese Patent Application No. 2009-055384, filed Mar. 9, 2009,the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the invention

The present invention relates to a signal correction apparatus whichadjusts the characteristics of an input signal.

2. Description of the Related Art

The development of a signal characteristic adjustment apparatus is inprogress. This signal characteristic adjustment apparatus provides adesired listening environment (for example, indoor transfercharacteristics, a head-related transfer function, and volume) byadjusting the characteristics (for example, volume, frequencycharacteristics, phase characteristics, and tone characteristics) of aninput signal (for example, a speech or audio signal) using digitalsignal processor (DSP).

For example, an equalizer apparatus is available. This apparatus canadjust a level (for example, a power or signal amplitude) for eachfrequency band. Upon adjusting the level of a digital signal, when thelevel is raised, it may exceed a maximum level of a digital signalsystem, and that signal may be unwantedly clipped.

For this reason, the equalizer apparatus which raises the level of adigital signal executes level control for declining the level of aninput signal over an entire frequency range, converts the input signalinto an analog signal, and then amplifies the level of the analog signalby an amount declined in the processing of the digital signal.

Conventionally, in order to prevent deterioration of the signal-to-noiseratio due to raising/declining of the level, the level of an inputdigital signal is declined based on a maximum level change so as not tobe clipped (for example, see Jpn. Pat. Appln. KOKAI Publication No.2002-345075).

However, conventionally, since the level is declined only by a fixedamount, an excessive volume drop occurs depending on input signals.

The conventional signal characteristic adjustment apparatus declines thelevel of a digital signal so as to prevent the digital signal from beingclipped. However, since the level is declined only by a fixed amount, anexcessive volume drop may occur.

BRIEF SUMMARY OF THE INVENTION

The present invention has been made to solve the aforementionedproblems, and has as its object to provide a signal characteristicadjustment apparatus which can prevent an excessive volume drop whilepreventing a digital signal from being clipped.

In order to achieve the above object, according to the present inventiona signal characteristic adjustment apparatus, which adjustscharacteristics of an input signal, comprising: a level control unitwhich controls a level of the input signal; a characteristic adjustmentunit which adjusts characteristics of the input signal, the level ofwhich is controlled by the level control unit; and a coefficientcalculation unit which calculates a level control coefficient used toadjust level control of the level control unit and a characteristicadjustment coefficient used to adjust level control of thecharacteristic adjustment unit based on a feature quantity of the inputsignal.

As described above, according to the present invention, upon adjustingthe characteristics of an input signal, the level of the input signal iscontrolled based on feature quantities of the input signal beforeadjustment. Therefore, according to the present invention, sinceadaptive processing suited to an input signal can be made, a signalcharacteristic adjustment apparatus and signal characteristic adjustmentmethod, which can prevent a digital signal from being clipped whilesuppressing its excessive volume drop can be provided.

Additional objects and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. The objectsand advantages of the invention may be realized and obtained by means ofthe instrumentalities and combinations particularly pointed outhereinafter.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention, andtogether with the general description given above and the detaileddescription of the embodiments given below, serve to explain theprinciples of the invention.

FIG. 1 is a block diagram showing the arrangement of a signalcharacteristic adjustment apparatus according to an embodiment of thepresent invention;

FIG. 2 is a graph showing an example of a provisional level controlcoefficient TH set by a balance setting unit of the signalcharacteristic adjustment apparatus according to the embodiment of thepresent invention;

FIG. 3 is a graph showing an example of provisional characteristicadjustment coefficients G_(p)(ω) set by the balance setting unit of thesignal characteristic adjustment apparatus according to the embodimentof the present invention;

FIG. 4 is a block diagram showing an example of the arrangement of acoefficient calculation unit 102A of the signal characteristicadjustment apparatus according to the embodiment of the presentinvention;

FIG. 5 is a block diagram showing the arrangement of a signalcharacteristic adjustment apparatus according to the second embodimentof the present invention;

FIG. 6 is a block diagram showing an example of the arrangement of acoefficient calculation unit 102A′ of the signal characteristicadjustment apparatus according to the second embodiment of the presentinvention;

FIG. 7 is a block diagram showing the arrangement of a signalcharacteristic adjustment apparatus according to the third embodiment ofthe present invention;

FIG. 8 is a block diagram showing an example of the arrangement of acoefficient calculation unit 102Aa of the signal characteristicadjustment apparatus according to the third embodiment of the presentinvention; and

FIG. 9 is a flowchart for explaining the processing of a signalcorrection apparatus according to an embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention will be described hereinafter withreference to the accompanying drawing.

First Embodiment

FIG. 1 shows the arrangement of a signal characteristic adjustmentapparatus according to an embodiment of the present invention. Thissignal characteristic adjustment apparatus includes a signal correctionunit 101 and characteristic adjustment unit 102. This signalcharacteristic adjustment apparatus is mounted in an electronicapparatus such as a speech communication apparatus (e.g., a cell phone)or a portable audio player, which converts a digital audio signal intoan analog signal and outputs the analog signal.

For example, in case of the speech communication apparatus, acommunication apparatus equipped in a stage before this circuitestablishes a communication link with a communication apparatus as acommunication partner to allow two-way speech communications with thecommunication partner. Reception data received by a wirelesscommunication unit is decoded by a decoder (not shown) into a digitalsignal as input signals x[n] (n=1, 2, 3, . . . , N) for eachpredetermined processing time unit (one frame=N samples).

The input signal x[n] may be either a speech or audio signal. In thefollowing description, assume that the input signal x[n] is a speechsignal. As for the range of N, N may be an integer greater than or equalto 1, and the range corresponds to, for example, a frame size of N=160samples. Note that in case of an audio signal, N mainly uses the powersof 2 such as 1024, 512, and 256.

Also, assume that the input signal x[n] is a 16-bit signal. However, thepresent invention is not limited to this, and the input signal x[n] maybe a b-bit signal (b=1, 2, 3, . . .) or a floating (floating point)signal. In the following description, the same condition applies tosignals to be described later (for example, d[n], y[n], and the like inFIG. 1).

The signal correction unit 101 and characteristic adjustment unit 102will be described below. The signal correction unit 101 receives theinput signals x[n] as inputs, and outputs output signals y[n] (n=1, 2,3, . . . , N) obtained by adjusting the characteristics of the inputsignals.

The signal correction unit 101 includes a digital level correction unit101A and characteristic correction unit 101B. The digital levelcorrection unit 101A executes dynamic range control (DRC) of the inputsignals x[n] using a level control coefficient TN input from thecharacteristic adjustment unit 102 (described later) to control thelevels (for example, amplitude or power) of the signals, and outputssignals d[n]. More specifically, when the level of each input signalx[n] is greater than the level control coefficient TH, the digital levelcorrection unit 101A attenuates the level of the input signal x[n]according to the level control coefficient TH, and outputs theattenuated signal.

On the other hand, when the level of each input signal x[n] is less thanthe level control coefficient TH, the digital level correction unit 101Aoutputs the input signal x[n] intact without changing its level. Withthe aforementioned control, only a large signal having a level whichwill cause clipping can be suppressed in advance. Note that the digitallevel correction unit 101A need only control the level of a signal, andmay be implemented as automatic gain adjustment (AGC).

The characteristic correction unit 101B adjusts the frequencycharacteristics of the signals d[h] using characteristic adjustmentcoefficients G(ω) input from the characteristic adjustment unit 102(described later). This adjustment can compensate for a frequency bandwhose characteristics deteriorate of a cell phone handset loudspeaker,cell phone hand-free loudspeaker, or the like, or gives special effectssuch as indoor transfer characteristics and a head-related transferfunction, thus realizing a desired listening environment.

More specifically, the characteristic correction unit 101B transformsthe signals d[n] into those of a frequency domain, and multiplies thetransformed signals by the characteristic adjustment coefficients G(ω).The characteristic correction unit 101B then outputs signals obtained bytransforming these products into signals of a time domain as the outputsignals y[n]. The output signals y[n] are converted into an analogsignal by a subsequent digital-to-analog converter (not shown), and theanalog signal is amplified and output from a loudspeaker via anamplifier. Note that the characteristic correction unit 101B may adjustthe frequency characteristics without transforming signals into those ofthe frequency domain.

In the description of this embodiment, the signal correction unit 101controls the level of a signal. However, the signal correction unit 101need only adjust the characteristics (for example, volume, frequencycharacteristics, phase characteristics, or tone characteristics) of asignal such as a speech or audio signal, and the present invention isnot limited to the aforementioned arrangement.

The characteristic adjustment unit 102 receives the input signals x[n]as inputs, and outputs the level control coefficient TH andcharacteristic adjustment coefficients G(ω) (ω=1, 2, 3, . . .) which areused to prevent clipping while suppressing an excessive volume drop of asignal for each frame. Note that the level control coefficient TH is aparameter which is used by the digital level correction unit 101A and isrequired to amplify or attenuate the level (for example, an amplitude orpower) of a signal so as to attain desired volume settings. When eachinput signal x[n] is a 16-bit speech signal, the level controlcoefficient TH falls within the range from 0 to 2¹⁶⁻¹.

On the other hand, each characteristic adjustment coefficients G(ω) is aparameter which is used by the characteristic correction unit 101B andis required to adjust the frequency characteristics. ω indicates afrequency bin number, but it may indicate a grouped frequency band.These level control coefficient TH and characteristic adjustmentcoefficients G(ω) are output for each frame. For this reason, the signalcorrection. unit 101 can execute adaptive processing suited to inputsignals, and can prevent clipping while suppressing an excessive volumedrop of a signal.

The characteristic adjustment unit 102 includes a coefficientcalculation unit 102A, balance setting unit 1023, digital leveladjustment unit 102C, and characteristic adjustment unit 1029. Therespective units will be described below.

The coefficient calculation unit 102A receives the input signals x[n] asinputs, and outputs a threshold of a minimum level control coefficientrequired to prevent clipping as a coefficient p. The coefficient p iscalculated based on an equation of square error minimum reference whichis prepared by executing, in advance, regression analysis of the featurequantities (for example, an absolute spectral power, average spectralpower, average spectral variance, spectral power standard deviation,average amplitude, maximum amplitude, zero-crossing count, amplitudevariance, amplitude standard deviation, inter-sample amplitudedifference variance, inter-sample amplitude difference standarddeviation, and so forth), and the threshold of the minimum level controlcoefficient required to prevent clipping.

As a result, since the adaptive processing suited to input signals foreach frame can calculate the coefficient p, clipping can be preventedwhile suppressing an excessive volume drop of a signal. A practicalarrangement example of the coefficient calculation unit 102A will bedescribed in detail later.

The balance setting unit 102B receives the coefficient p as an input,and makes a conditional evaluation so as to prevent speech qualitydeterioration (for example, distortions caused by the level control, anunnatural volume caused by the automatic volume control, noise, and thelike). The balance setting unit 102B outputs a provisional level controlcoefficient TH_(p) and provisional characteristic adjustmentcoefficients G_(p)(ω) based on the conditional evaluation result.

Note that the conditional evaluation is to compare a value which is setin advance as a minimum level control coefficient TN (minimum levelcontrol coefficient α) with the coefficient p. Note that this minimumlevel control coefficient α is set to prevent speech qualitydeterioration due to the level control of a signal. Since theprovisional level control coefficient TH_(p) and provisionalcharacteristic adjustment coefficients G_(p)(ω) are calculated based onthe comparison result between the minimum level control coefficient aand the coefficient p, clipping can be prevented while suppressingspeech quality deterioration caused by the level control and anexcessive volume drop.

A practical operation of the balance setting unit 102B will be describedbelow. The balance setting unit 102B compares the coefficient p andminimum level control coefficient a. When the coefficient p is greaterthan the minimum level control coefficient a (α<p), since this meansthat the level control does not cause any speech deterioration, thebalance setting unit 102B outputs the coefficient p as the provisionallevel correction coefficient TH_(p) intact, and outputs provisionalcharacteristic adjustment coefficients G₁(ω) one frame before as theprovisional characteristic adjustment coefficients

Note that the provisional characteristic adjustment coefficients G₁(ω)one frame before are the provisional characteristic adjustmentcoefficients G_(p)(ω) which were calculated one frame before the currentframe. Note that the provisional characteristic adjustment coefficientsG₁(ω) one frame before need only be information used to adjust thecharacteristics one frame before the current frame, and may be, forexample, characteristic adjustment coefficients G(ω) calculated oneframe before the current frame.

On the other hand, when the coefficient p is less than the minimum levelcontrol coefficient α (α>p), since this means that the level controlcauses speech deterioration, the balance setting unit 102B outputs theminimum level control coefficient α as the provisional level correctioncoefficient TH_(p). Also, the balance setting unit 102B suppresses theamplification and attenuation levels of the provisional characteristicadjustment coefficients G₁(ω) one frame before, as given by:

$\begin{matrix}{{G_{p}(\omega)} = {{G_{1}(\omega)} - {\left( {1 - \frac{\beta \cdot p}{\alpha}} \right)\left( {{G_{1}(\omega)} - 1} \right)}}} & (1)\end{matrix}$

and outputs the provisional characteristic adjustment coefficients G₁(ω)one frame before, the levels of which are suppressed, as the provisionalcharacteristic adjustment coefficients G_(p)(ω).

Note that β in Equation 1 is a parameter used to weight a suppressioneffect of the amplification and attenuation levels of the provisionalcharacteristic adjustment coefficients G_(p)(ω), and β≦1. When theminimum level control coefficient α is much greater than the balancecontrol coefficient p(α>>p), the amplification and attenuation levels ofthe provisional characteristic adjustment coefficients G_(p)(ω) arelargely suppressed to apply processing that nearly does not change thecharacteristics. When the minimum level control coefficient α is equalto the coefficient p, the correction coefficients TH_(p) and G_(p)(ω)may be determined based on either of the aforementioned conditions.

FIG. 2 is a graph showing an example of the provisional level controlcoefficient TH_(p) set by the balance setting unit 102B. When thecoefficient p is greater than the minimum level control coefficient α(1) (α<p), the provisional level control coefficient TH_(p) is decidedbased on the coefficient p. Note that the provisional level controlcoefficient TH_(p) need only be calculated based on the coefficient p,and may not be output as the coefficient p intact.

On the other hand, when the coefficient p is less than the minimum levelcontrol coefficient α(2)(α>p), the provisional level control coefficientTH_(p) is decided based on the minimum level control coefficient α. Notethat the provisional level control coefficient TH_(p) need only becalculated based on the minimum level control coefficient α, and may notbe output as the minimum level control coefficient α intact.

FIG. 3 is a graph showing an example of the provisional characteristicadjustment coefficients G_(p)(ω) set by the balance setting unit 102B.When the coefficient p is greater than the minimum level controlcoefficient α(1)(α<p), the provisional characteristic adjustmentcoefficients G_(p)(ω) are decided based on the provisionalcharacteristic adjustment coefficients G₁(ω) one frame before.

Note that the provisional characteristic adjustment coefficientsG_(p)(ω) need only be calculated based on the provisional characteristicadjustment. coefficients G₁(ω)one frame before, and they may not beoutput as the provisional characteristic adjustment coefficients G₁(ω)one frame before intact.

On the other hand, when the coefficient p is less than the minimum levelcontrol coefficient α(2)(α>p), the provisional characteristic adjustmentcoefficients G_(p)(ω) are output after suppressing the amplification andattenuation levels of the provisional characteristic adjustmentcoefficients G₁(ω) one frame before. Note that the provisionalcharacteristic adjustment coefficients G_(p)(ω) need only be output bysuppressing the amplification and attenuation levels of the provisionalcharacteristic adjustment coefficients G₁(ω) one frame before, and arenot limited to Equation 1.

The digital level adjustment unit 102C receives the provisional levelcontrol coefficient TH_(p) as an input, and suppresses transientinfluences between neighboring frames. More specifically, the digitallevel adjustment unit 102C applies smoothing processing to theprovisional level control coefficients TH_(p) between neighboring framesusing:

$\begin{matrix}\begin{matrix}{{TH} = {{smooth}\left( {{TH}_{R},{\ldots \mspace{14mu} {TH}_{2}},{TH}_{1},{TH}_{p}} \right)}} \\{= \frac{{\sum\limits_{j = 1}^{R}{\phi \; {R_{j} \cdot {TH}_{j}}}} + {\phi \; {p \cdot {TH}_{p}}}}{{\sum\limits_{j = 1}^{R}{\phi \; R_{j}}} + {\phi \; p}}}\end{matrix} & (2)\end{matrix}$

and outputs the smoothed value to the digital level correction unit 101Aas the level control coefficient TH. More specifically, the digitallevel adjustment unit 102C calculates the level control coefficient THby smoothing the provisional level control coefficients TH_(p) accordingto Equation 2 so as to suppress transient influences between neighboringframes with respect to the R-th and subsequent frames, and outputs thecalculated level control coefficient TH.

Note that the digital level correction unit 101A outputs a level controlcoefficient TH₀ from the beginning of speech communication until theR-th frame (R≧1). TH₀ is the level control coefficient TH which was setin the digital level correction unit 101A in advance before thebeginning of speech communication. In Equation 2, TH_(R) is the levelcontrol coefficient TH calculated R frames before. However, TH_(R) needonly be information used to control the signal level R frames beforeand, for example, it may be the provisional level control coefficientTH_(p) R frames before.

Also, φR_(j) and φp are smoothing coefficients, which allow to changethe weights for the smoothing processing. Note that the digital leveladjustment unit 102C is not limited to the aforementioned example aslong as it suppresses transient influences between neighboring frames.

The characteristic adjustment unit 102D receives the provisionalcharacteristic adjustment coefficients G_(p)(ω) as inputs, appliessmoothing processing to the provisional characteristic adjustmentcoefficients G_(p)(ω) between neighboring frames, and outputs thecharacteristic adjustment coefficients G(ω) to be set in thecharacteristic correction unit 101B. More specifically, in order tosuppress transient influences between neighboring frames with respect tothe R-th and subsequent frames, the characteristic adjustment unit 102Dcalculates the characteristic adjustment coefficients G(ω) by smoothingthe provisional characteristic adjustment coefficients G_(p)(ω)according to:

$\begin{matrix}\begin{matrix}{{G(\omega)} = {{smooth}\left( {{G_{R}(\omega)},{\ldots \mspace{14mu} {G_{2}(\omega)}},{G_{1}(\omega)},{G_{p}(\omega)}} \right)}} \\{= \frac{{\sum\limits_{j = 1}^{R}{\phi \; {R_{j} \cdot {G_{j}(\omega)}}}} + {\phi \; {p \cdot {G_{p}(\omega)}}}}{{\sum\limits_{j = 1}^{R}{\phi \; R_{j}}} + {\phi \; p}}}\end{matrix} & (3)\end{matrix}$

and outputs the calculated characteristic adjustment coefficients G(ω).

Note that the characteristic correction unit 101B outputs characteristicadjustment coefficients G₀(ω) from the beginning of speech communicationuntil the R-th frame (R≧1). Coefficients G₀(ω) are the characteristicadjustment coefficients G(ω) which were set in the characteristiccorrection unit 101B in advance before the beginning of speechcommunication.

In Equation 3, provisional characteristic adjustment coefficientsG_(R)(ω) are characteristic adjustment coefficients G(ω) which werecalculated R frames before. However, the characteristic adjustmentcoefficients G_(R)(ω) need only be information used to adjust thecharacteristics of a signal R frames before, and may also be theprovisional characteristic adjustment coefficients G_(p)(ω) R framesbefore.

Also, φR_(j) and φp are smoothing coefficients, which allow to changethe weights for the smoothing processing. These smoothing coefficientsmay not be the same as those in the digital level adjustment unit 102C.Note that the characteristic adjustment unit 102D is not limited to theaforementioned example, as long as it suppresses transient influencesbetween neighboring frames.

FIG. 4 shows an arrangement example of the coefficient calculation unit102A. The coefficient calculation unit 102A includes a feature quantityextraction unit 102A1 and regression arithmetic unit 102A2. Furthermore,the feature quantity extraction unit 102A1 includes extraction units102A11 to 102A14. Respective units will be described below.

The feature quantity extraction unit 102A1 receives the input signalsx[n] as inputs, extracts a plurality of feature quantities (for example,an absolute spectral power, average spectral power, average spectralvariance, spectral power standard deviation, average amplitude, maximumamplitude, zero-crossing count, amplitude variance, amplitude standarddeviation, inter-sample amplitude difference variance, inter-sampleamplitude difference standard deviation, and so forth) for each frame,and outputs feature quantities A(m) (m=1, 2, 3, . . . S) (S≧1). In thefollowing description, the feature quantity extraction unit 102A1extracts four feature quantities, but it may extract one or more featurequantities.

The extraction unit 102A11 receives the input signals x[n] as inputs,and transforms the input signals x[n] from signals of a time domain intothose of a frequency domain. The extraction unit 102A11 calculatesspectral powers for respective frequency bins, calculates a spectralpower which is maximum of those of frequency band ω (maximum spectralpower), and outputs it as a feature quantity A(1). The extraction unit102A11 includes a frequency domain transformation unit 102A111, powercalculation unit 102A112, and maximum power calculation unit 102A113.

The frequency domain transformation unit 102A111 receives the inputsignals x[n] as inputs, transforms the input signals x[n] from signalsof the time domain into signals X(ω) of the frequency domain by, forexample, an arithmetic operation such as fast Fourier transformation(FFT), and outputs the transformed signals.

Note that the frequency domain transformation unit 102A111 mayalternatively use other orthogonal transformations represented bydiscrete Fourier transformation (DFT), discrete cosine transformation(DCT), Walsh-Hadamard transformation (WHT), Harr transformation (HT),slant transformation (SLT), and Karhunen-Loeve transformation (KLT),which transform signals into those of the frequency domain.

More specifically, assume that the input signals x[n] (n=1, 2, 3, . . ., N) are n samples of input signals of the time domain, and let N be thedegree of the FFT. Then, the signals X(ω) (ω=1, 2, 3, . . . , N) of thefrequency domain have N frequency bins. Note that signals to which theFFT is applied may overlap signals of the previous frame or may bezero-padded to convert the data length to the power of 2, thus settingthe degree N of the FFT to be the power of 2.

The power calculation unit 102A112 receives the frequency domain signalsX(ω) as inputs, calculates spectral powers for respective frequencybins, and outputs them as spectral powers pow(ω) (ω=1, 2, 3, . . . , N).The power calculation unit 102A112 calculates the spectral powers of theinput signals for respective frequency bins according to:

pow(ω)=|X(ω)|²  (4)

However, the output of the power calculation unit 102A112 may not be thesquare of X(ω) . Alternatively, using signals R frames (R being aninteger) before the current frame, spectral powers may be calculatedusing the average spectral power pow(ω), as given by:

$\begin{matrix}{{{pow}(\omega)} = \frac{\sum\limits_{i = 1}^{R}{{X_{i}(\omega)}}^{2}}{R}} & (5)\end{matrix}$

Using the average spectral power in this way, transient influencesbetween neighboring frames can be suppressed. Note that X_(i)(ω) inEquation 5 is a frequency domain signal X(ω) i frames (i=1, 2, 3, . . ., R) before the current frame. Also, each spectral power pow(ω) may beweighted by multiplying it by the characteristic adjustment coefficientG₀(ω), as given by:

pow(ω)=G ₀(ω)×pow(ω)  (6)

By weighting the spectral powers in this way, a more ideal coefficient pcan be calculated. Note that the spectral powers need only be calculatedfrom the frequency domain signals X(ω), and the present invention is notlimited to the aforementioned configuration.

The maximum power calculation unit 102A113 receives the spectral powerspow(ω) as inputs, detects a maximum spectral power pow_MAX from thespectral powers of all the frequency bins, and outputs it as the featurequantity A(1).

Alternatively, the maximum power calculation unit 102A113 may output, asthe feature quantity A(1), a value obtained by normalizing the spectralpower pow_MAX by the sum total of spectral powers of all the frequencybins according to:

$\begin{matrix}{{A(1)} = \frac{pow\_ MAX}{\sum\limits_{\omega = 1}^{N}{{pow}(\omega)}}} & (7)\end{matrix}$

In Equation 7, A(1) assumes a value less than or equal to 1 (A(1)≦1),and when A(1) is closer to this means that a spectral power isconcentrated on a specific frequency bin. If the characteristics are tobe adjusted to amplify a specific frequency bin, whether or not clippingoccurs can be determined in advance based on the feature quantity A(1)given by Equation 7. Note that the maximum power calculation unit102A113 is not limited to the above example as long as it calculates amaximum spectral power of spectral powers of all the frequency bins.

The extraction unit 102A12 receives the input signal x[n] as inputs, andtransforms the input signals x[n] from signals of the time domain intothose of the frequency domain. The extraction unit 102A12 calculatesspectral powers for respective frequency bins, calculates the average(average spectral power) of these spectral powers, and outputs it as afeature quantity A(2). The extraction unit 102A12 includes a frequencydomain transformation unit 102A111, power calculation unit 102A112, andaverage power calculation unit 102A121.

The average power calculation unit 102A121 receives the spectral powerspow(ω) as inputs, detects the average pow_AVG (average spectral power)of the spectral powers for respective frequency bins, and outputs it asthe feature quantity A(2). More specifically, the average powercalculation unit 102A121 calculates the feature quantity A(2) accordingto:

$\begin{matrix}\begin{matrix}{{A(2)} = {pow\_ AVG}} \\{= \frac{\sum\limits_{\omega = 1}^{N}{{pow}(\omega)}}{N}}\end{matrix} & (8)\end{matrix}$

The extraction unit 102A13 receives the input signals x[n] as inputs,calculate the amplitude variance, and outputs it as a feature quantityA(3). The extraction unit 102A13 includes an amplitude variancecalculation unit. 102A131. The amplitude variance calculation unit102A131 receives the input signals x[n] as inputs, calculates theamplitude variance, and outputs it as the feature quantity A(3). Morespecifically, the amplitude variance calculation unit 102A131 calculatesthe feature quantity A(3) according to:

$\begin{matrix}{{A(3)} = \frac{\sum\limits_{n = 1}^{N}{{{x\lbrack n\rbrack} - {mean}}}^{2}}{N}} & (9)\end{matrix}$

where “mean” is the mean value of x[n].

The extraction unit 102A14 receives the input signals x[n] as inputs,calculates a zero-crossing count, and outputs it as a feature quantityA(4). The extraction unit 102A14 includes a zero-crossing countcalculation unit 102A141.

The zero-crossing count calculation unit 102A141 receives the inputsignals x[n] as inputs, counts a zero-crossing count (a case of x[n]=0and a case in which the sign of x[n] is inverted), and outputs it as afeature quantity A(4). Since the zero-crossing count is used indetermination of, e.g., voiced speech/unvoiced speech, clipping can beprevented while suppressing an excessive volume drop of a signaldepending on voiced speech or unvoiced speech.

The regression arithmetic unit 102A2 calculates the threshold of theminimum level control coefficient required to prevent clipping from thefeature quantities. For example, the regression arithmetic unit 102A2receives the feature quantities A(m) (m=1, 2, 3, 4) as inputs, andoutputs the coefficient p based on an equation of square error minimumreference prepared by executing, in advance, regression analysis of thethreshold and feature quantities, like Equation 10:

$\begin{matrix}{p = {\sum\limits_{m = 1}^{4}{{\zeta (m)} \cdot {A(m)}}}} & (10)\end{matrix}$

In Equation 10,ζ(m) (m=1, 2, 3, 4) is coefficient obtained by theregression analysis of the threshold of the minimum level controlcoefficient required to prevent clipping and the feature quantitiesA(m), and can be set in advance. If the correlation between thethreshold of the minimum level control coefficient required to preventclipping and the feature quantities A(m) is high, a more idealcoefficient p can be calculated.

As described above, in the signal characteristic adjustment apparatuswith the above arrangement, the coefficient calculation unit 102Acalculates the coefficient p according to the input signals x[n], andthe balance setting unit 102B decides, from the calculated coefficientp, the coefficient used to control the level of a signal in the digitallevel correction unit 101A, and the coefficient used to adjust thecharacteristics of a signal in the characteristic correction unit 101B.Therefore, according to the signal characteristic adjustment apparatuswith the above arrangement, the digital level correction unit 101A andcharacteristic correction unit 101B can execute adaptive processingaccording to the coefficient p of the input signals x[n], thuspreventing an excessive volume drop while preventing a digital signalfrom being clipped.

Second Embodiment

The second embodiment according to the present invention will bedescribed below. FIG. 5 shows the arrangement of the second embodiment.In the following description, the same reference numbers denote the samecomponents as in the aforementioned first embodiment, and a repetitivedescription thereof will be avoided as needed for simplicity.

A characteristic adjustment unit 102 according to the second embodimentreceives, as inputs, output signals y[n] obtained by adjusting thecharacteristics of signals in addition to input signals x[n], andoutputs a level control coefficient TH and characteristic adjustmentcoefficients G(ω) (ω=1, 2, 3, . . . ) which are required to preventclipping while suppressing an excessive volume drop of a signal for eachframe. The characteristic adjustment unit 102 according to the secondembodiment uses a coefficient calculation unit 102A′ in place of thecoefficient calculation unit 102A used in the characteristic adjustmentunit 102 according to the first embodiment.

The coefficient calculation unit 102A′ receives, as inputs, the outputsignals y[n] obtained by adjusting the characteristics of signals inaddition to the input signals x[n], and outputs a threshold of a minimumlevel control coefficient required to prevent clipping as a coefficientp. As shown in FIG. 6, the coefficient calculation unit 102A′ includes afeature quantity extraction unit 102A′1 and regression arithmetic unit102A′2.

The feature quantity extraction unit 102A′1 includes extraction units102A′11, 102A′12, 102A′13, and 102A′14. The feature quantity extractionunit 102A′1 receives, as inputs, the output signals y[n] obtained byadjusting the characteristics of signals in addition to the inputsignals x[n], extracts a plurality of feature quantities for each frame,and outputs feature quantities A(m) and B(m) (m=1, 2, 3, 4).

In the following description, the feature quantity extraction unit102A′1 extracts four feature quantities. However, one or more types offeature quantities including an absolute spectral power, averagespectral power, average spectral variance, spectral power standarddeviation, average amplitude, maximum amplitude, zero-crossing count,amplitude variance, amplitude standard deviation, inter-sample amplitudedifference variance, inter-sample amplitude difference standarddeviation, and so forth need only be calculated.

The extraction unit 102A′11 receives, as inputs, the input signals x[n]and the output signals y[n] obtained by changing the characteristics ofsignals, and transforms those signals from signals of a time domain intothose of a frequency domain. The extraction unit 102A′11 calculatesspectral powers for respective frequency bins, calculates spectralpowers which are maximum of those of frequency bins ω (maximum spectralpowers), and outputs them as feature quantities A(1) and B(1). Theextraction unit 102A′11 includes a frequency domain transformation unit102A′111, power calculation unit 102A′112, and maximum power calculationunit 102A′113.

The frequency domain transformation unit 102A′111 receives, as inputs,the output signals y[n] obtained by adjusting the characteristics ofsignals in addition to the input signals x[n]. The frequency domaintransformation unit 102A′111 transforms the input signals x[n] fromsignals of the time domain into signals X(ω) of the frequency domain andtransforms the output signals y[n] from signals of the time domain intosignals Y(ω) of the frequency domain by, for example, an arithmeticoperation such as fast Fourier transformation (FFT).

The power calculation unit 102A′112 receives, as inputs, the frequencydomain signals Y(ω) in addition to the frequency domain signals X(ω),calculates spectral powers for respective frequency bins, and outputsthem as spectral powers xpow(ω) and ypow(ω) (ω=1, 2, 3, . . . , N). Thepower calculation unit 102A′112 calculates the spectral powers of theinput signals for respective frequency bins according to:

xpow(ω)=|X(ω)|²  (11)

ypow(ω)=|Y(ω)|²  (12)

The maximum power calculation unit 102A′113 receives, as inputs, thespectral powers xpow(ω) and ypow(ω), detects maximum spectral powersxpow_MAX and ypow_MAX from the spectral powers of all the frequencybins, and outputs them as the feature quantities A(1) and B(1).

The extraction unit 102A′12 receives, as inputs, the input signal x[n]and the output signals y[n] obtained by adjusting the characteristics ofsignals, and transforms those signals from signals of the time domaininto those of the frequency domain. The extraction unit 102A′12calculates spectral powers for respective frequency bins, calculatesaverages (average spectral powers) of these spectral powers, and outputsthem as feature quantities A(2) and B(2). The extraction unit 102A′12includes a frequency domain transformation unit 102A′111, powercalculation unit 102A′112, and average power calculation unit 102A′121.

The average power calculation unit 102A′121 receives, as inputs, thespectral powers xpow(ω) and ypow(ω), detects averages xpow_AVG andypow_AVG (average spectral powers) of the spectral powers for respectivefrequency bins, and outputs them as the feature quantities A(2) andB(2). More specifically, the average power calculation unit 102A′121calculates the feature quantities A(2) and B(2) according to:

$\begin{matrix}\begin{matrix}{{A(2)} = {xpow\_ AVG}} \\{= \frac{\sum\limits_{\omega = 1}^{N}{{xpow}(\omega)}}{N}}\end{matrix} & (13) \\\begin{matrix}{{B(2)} = {ypow\_ AVG}} \\{= \frac{\sum\limits_{\omega = 1}^{N}{{ypow}(\omega)}}{N}}\end{matrix} & (14)\end{matrix}$

The extraction unit 102A′13 receives, as inputs, the input signals x[n]and the output signals y[n] obtained by adjusting the characteristics ofsignals, calculates amplitude variances, and outputs them as featurequantities A(3) and B(3). The extraction unit 102A13 includes anamplitude variance calculation unit 102A131. The amplitude variancecalculation unit 102A′131 receives, as inputs, the output signals y[n]obtained by adjusting the characteristics of signals in addition to theinput, signals x[n], calculates amplitude variances, and outputs them asthe feature quantities A(3) and B(3).

More specifically, the amplitude variance calculation unit 102A′131calculates the feature quantities A(3) and B(3) according to:

$\begin{matrix}{{A(3)} = \frac{\sum\limits_{n = 1}^{N}{{{x\lbrack n\rbrack} - {x{mean}}}}^{2}}{N}} & (15) \\{{B(3)} = \frac{\sum\limits_{n = 1}^{N}{{{y\lbrack n\rbrack} - {y{mean}}}}^{2}}{N}} & (16)\end{matrix}$

Note that “xmean” in Equation 15 is the mean value of x[n], and “ymean”in Equation 16 is the mean value of y[n].

The extraction unit 102A′14 receives, as inputs, the output signals y[n]obtained by adjusting the characteristics of signals in addition to theinput signals x[n], calculates zero-crossing counts, and outputs them asfeature quantities A(4) and B(4). The extraction unit 102A′14 includes azero-crossing count calculation unit 102A′141.

The zero-crossing count calculation unit 102A′141 receives, as inputs,the output signals y[n] obtained by adjusting the characteristics ofsignals in addition to the input signals x[n], counts zero-crossingpoints (a case of x[n]=0 and y[n]=0, and a case in which the sign ofx[n] and y[n] is inverted), and outputs them as feature quantities A(4)and B(4). Since the zero-crossing count is used in determination of,e.g., voiced speech/unvoiced speech, clipping can be prevented whilesuppressing an excessive volume drop of a signal depending on voicedspeech or unvoiced speech.

The regression arithmetic unit 102A′2 receives the feature quantitiesA(m) and B(m) (m=1, 2, 3, 4) as inputs, and outputs the coefficient pbased on an equation of square error minimum reference prepared byexecuting, in advance, regression analysis of these feature quantities.More specifically, the regression arithmetic unit 102A′2 calculates thecoefficient p using:

$\begin{matrix}{p = {\sum\limits_{m = 1}^{S}\left( {{{\zeta^{\prime}(m)} \cdot {A(m)}} + {{\xi^{\prime}(m)} \cdot {B(m)}}} \right)}} & (17)\end{matrix}$

In Equation 17, ζ′(m) and ε(m) (m=1, 2, 3, . . . , S) are coefficientsobtained by executing, in advance, the regression analysis of thethreshold of the minimum level control coefficient required to preventclipping and the feature quantities A(m) and B(m), and can be set inadvance. Since the feature quantities B(m) are those of the outputsignals obtained by adjusting the characteristics of signals, whether ornot signals are clipped is easily determined. This means that thecorrelation with the threshold of the minimum level control coefficientrequired to prevent clipping is high, and an ideal coefficient p can becalculated.

With this arrangement as well, the same effects as in the firstembodiment can be obtained. Also, according to this arrangement, sincethe feature quantities B(m) are used, whether or not signals are clippedis easily determined. Since the correlation with the threshold of theminimum level control coefficient required to prevent clipping is high,more ideal coefficient p can be calculated. That is, since the digitallevel correction unit 101A and characteristic correction unit 101B canexecute adaptive processing with higher precision according to thiscoefficient p, an excessive volume drop can be prevented whileeffectively preventing a digital signal from being clipped.

Third Embodiment

The third embodiment according to the present invention will bedescribed below. FIG. 7 shows the arrangement of the third embodiment.In the following description, the same reference numbers denote the samecomponents as in the aforementioned embodiments, and a repetitivedescription thereof will be avoided as needed for simplicity.

As shown in FIG. 7, a signal correction unit 101 of the third embodimentincludes an automatic volume correction unit 101C in addition to adigital level correction unit 101A and characteristic correction unit101B. In the following description, the automatic volume correction unit101C is arranged after the digital level correction unit 101A, but itmay be arranged before the digital level correction unit 101A or afterthe characteristic correction unit 101B.

The automatic volume correction unit 101C receives signals d[n] asinputs, executes automatic volume control, and outputs the controlledsignals as signals z[n]. The volume to be automatically set is decidedbased on volume control coefficients GAIN[n] (described in detail later)output from a characteristic adjustment unit 102 (described later). Thatis, the automatic volume correction unit 101C amplifies or attenuatesthe signals d[n] according to the volume control coefficients GAIN[n] toobtain the output signals z[n] (z[n]=GAIN[n]·d[n]). In this way, thevolume can be automatically adjusted to an appropriate speech pressure(one that feels comfortable).

The characteristic adjustment unit 102 uses a coefficient calculationunit 102Aa in place of the coefficient calculation unit 102A used in thecharacteristic adjustment unit 102 described in the above embodiment.Also, the characteristic adjustment unit 102 uses a balance setting unit102Ba in place of the balance setting unit 102B. Furthermore, thecharacteristic adjustment unit 102 uses an automatic volume adjustmentunit 102E.

As shown in FIG. 8, the coefficient calculation unit 102Aa receivesinput signals x[n] as inputs, outputs a threshold of a minimum levelcontrol coefficient required to prevent clipping as a coefficient p1,and also outputs a coefficient used to attain automatic volume controlas a coefficient p2. The coefficient calculation unit 102Aa includes afeature quantity extraction unit 102A and regression arithmetic unit102A2 a.

The regression arithmetic unit 102A2 a receives feature quantities A(m)(m=1, 2, 3, 4) as inputs, and outputs the coefficients p1 and p2 basedon equations of square error minimum reference prepared by executing, inadvance, regression analysis of the feature quantities. Morespecifically, the regression arithmetic unit 102A2 a calculates thecoefficients p1 and p2 using:

$\begin{matrix}{{p\; 1} = {\sum\limits_{m = 1}^{4}{\zeta \; {{a(m)} \cdot {A(m)}}}}} & (18) \\{{p\; 2} = {\sum\limits_{m = 1}^{4}{\xi \; {{a(m)} \cdot {A(m)}}}}} & (19)\end{matrix}$

In Equation 18, ζa(m) (m=1, 2, 3, 4) is a coefficient obtained byexecuting, in advance, the regression analysis of the threshold of theminimum level control coefficient required to prevent clipping and thefeature quantities A(m), and can be set in advance.

In Equation 19, εa(m) (m=1, 2, 3, 4) is a coefficient obtained byexecuting, in advance, the regression analysis of a coefficient used toautomatically adjust to an appropriate speech pressure and the featurequantities A(m), and can be set in advance. If the correlation among thethreshold of the minimum level control coefficient required to preventclipping, the coefficient used to automatically adjust to an appropriatespeech pressure, and the feature quantities A(m) is high, more idealcoefficients p1 and p2 can be calculated.

The balance setting unit 102Ba receives the coefficients p1 and p2 asinputs, makes a conditional evaluation so as not to cause speech qualitydeterioration (for example, distortions caused by the level control, anunnatural volume caused by the automatic volume control, noise, and thelike), and outputs a provisional level control coefficient TH_(p′),provisional characteristic adjustment coefficients G_(p)(ω), and aprovisional volume control coefficient GAINp based on the evaluationresult.

Note that the provisional level control coefficient TH_(p) andprovisional characteristic adjustment coefficients G_(p)(ω) are decidedaccording to the coefficient p1 by the same processing as in the firstembodiment. As for the provisional volume control coefficient GAINp, theabsolute value |GAINp₁−p2| of the difference between a provisionalvolume control coefficient GAINp₁, which was decided one frame beforethe current frame, and the coefficient p2 is calculated, and when thisabsolute value is less than an upper limit y (|GAINp₁−p2|<γ), theprovisional volume control coefficient GAINp is output as thecoefficient p2 intact.

On the other hand, when the absolute value is greater than the upperlimit γ (|GAINp₁−p2|>γ), the provisional volume control coefficientGAINp is limited to γ when it is output. Note that the upper limit γ isa volume difference that audibly poses no problem, and is about 3 dB ingeneral. However, the upper limit γ is not limited to this. By executingsuch processing, speech quality deterioration (unnatural volume causedby the automatic volume control) can be prevented.

The automatic volume adjustment unit 102E receives the provisionalvolume control coefficient GAINp as an input, executes smoothingprocessing for respective samples to suppress transient influencesbetween neighboring frames, and outputs the volume control coefficientsGAIN[n] to be set in the automatic volume correction unit 101C. Morespecifically, the automatic volume adjustment unit 102E calculates thevolume control coefficients GAIN[n] according to:

$\begin{matrix}{{{GAIN}\lbrack n\rbrack} = {{smooth}\left( {{{GAIN}\left\lbrack {n - L} \right\rbrack},\ldots \mspace{14mu},{{GAIN}\left\lbrack {n - 1} \right\rbrack},{GAIN}_{p}} \right)}} \\{= \frac{{\sum\limits_{j = 1}^{L}{\phi \; {{Lj} \cdot {{GAIN}\left\lbrack {n - j} \right\rbrack}}}} + {\phi \; {p \cdot {GAIN}_{p}}}}{{\sum\limits_{j = 1}^{S}{\phi \; {Lj}}} + {\phi \; p}}}\end{matrix}$

Note that the volume control coefficients GAIN[n] can be calculatedusing volume control coefficients GAIN[n-L] to GAIN[n-1], which werecalculated S samples before the current sample. Also, φLj and φp aresmoothing coefficients, which allow to change weights for the smoothingprocessing. Note that the automatic volume adjustment unit 102E needonly suppress transient influences between neighboring frames, and mayuse, for example, smoothing processing described in Jpn. Pat. Appln.KOKAI Publication No. P2007-93827.

With this arrangement as well, the same effects as in the aboveembodiments can be obtained. Also, according to this arrangement, thevolume can automatically adjusted to an appropriate speech. pressure inaddition to prevention of an excessive volume drop while preventing adigital signal from being clipped.

Note that the present invention is not limited to the aforementionedembodiments intact, and can be embodied by modifying requiredconstituent elements without departing from the scope of the inventionwhen it is practiced. By appropriately combining a plurality of requiredconstituent elements disclosed in the embodiments, various inventionscan be formed. For example, some of all the required constituentelements disclosed in the embodiments may be deleted.

Furthermore, required constituent elements described in differentembodiments may be appropriately combined. For example, in the aboveembodiments, the characteristic adjustment unit 102 executes signalprocessing using a plurality of functional blocks, as shown in FIG. 2.Alternatively, the characteristic adjustment unit 102 can be implementedby one chip using a processor such as a DSP.

In this case, the characteristic adjustment unit 102 can execute signalprocessing according to the flowchart shown in FIG. 9. That is, afterinput signals x[n] are acquired in step 2 a, the characteristicadjustment unit 102 executes the same processing as that of the featurequantity extraction unit 102A1 in step 2 b, and executes the sameprocessing as that of the regression arithmetic unit 102A2 in step 2 c.Then, the characteristic adjustment unit 102 executes the sameprocessing as that of the balance setting unit 102B in step 2 d, andexecutes the same processes as those of the digital level adjustmentunit 102C and characteristic adjustment unit 102D in step 2 e.

In addition, even when various modifications may be made withoutdeparting from the scope of the present invention, the present inventioncan be carried out.

Additional advantages and modifications will readily occur to thoseskilled in the art. Therefore, the invention in its broader aspects isnot limited to the specific details and representative embodiments shownand described herein. Accordingly, various modifications may be madewithout departing from the spirit or scope of the general inventiveconcept as defined by the appended claims and their equivalents.

1. A signal characteristic adjustment apparatus, which adjustscharacteristics of an input signal, comprising: a level control unitwhich controls a level of the input signal; a characteristic adjustmentunit which adjusts characteristics of the input signal, the level ofwhich is controlled by the level control unit; and a coefficientcalculation unit which calculates a level control coefficient used toadjust. level control of the level control unit and a characteristicadjustment coefficient used to adjust characteristic control of thecharacteristic adjustment unit based on a feature quantity of the inputsignal.
 2. The apparatus according to claim 1, wherein the coefficientcalculation unit calculates a clipping prevention coefficient used toprevent clipping based on the feature quantity of the input signal,decides the level control coefficient based on the clipping preventioncoefficient when the calculated clipping prevention coefficient isgreater than a pre-set threshold, and decides the level controlcoefficient based on the pre-set threshold when the calculated clippingprevention coefficient is less than the pre-set threshold.
 3. Theapparatus according to claim 1, wherein the coefficient calculation unitcalculates a clipping prevention coefficient used to prevent clippingbased on the feature quantity of the input signal, decides thecharacteristic adjustment coefficient based on a previously adjustedcharacteristic adjustment coefficient when the calculated clippingprevention coefficient is greater than a pre-set threshold, and decidesthe characteristic adjustment coefficient based on a difference betweenthe pre-set threshold and the clipping prevention coefficient when thecalculated clipping prevention coefficient is less than the pre-setthreshold.
 4. The apparatus according to claim 2, wherein thecoefficient calculation unit calculates the clipping preventioncoefficient used to prevent clipping based on the feature quantity ofthe input signal, decides the characteristic adjustment coefficientbased on a previously adjusted characteristic adjustment coefficientwhen the calculated clipping prevention coefficient is greater than thepre-set threshold, and decides the characteristic adjustment coefficientbased on a difference between the pre-set threshold and the clippingprevention coefficient when the calculated clipping preventioncoefficient is less than the pre-set threshold.
 5. The apparatusaccording to claim 2, wherein the coefficient calculation unitcalculates the clipping prevention coefficient based on featurequantities extracted from the input signal and an equation of squareerror minimum reference, which is prepared by executing, in advance,regression analysis of the feature quantities.
 6. The apparatusaccording to claim 2, wherein the coefficient calculation unit comprisesa feature quantity regression analysis unit which calculates theclipping prevention coefficient based on feature quantities of the inputsignal, feature quantities of the input signal, the characteristics ofwhich are adjusted by the characteristic adjustment unit, and anequation of square error minimum reference, which is prepared byexecuting, in advance, regression analysis of the feature quantities. 7.A signal characteristic adjustment apparatus, which adjustscharacteristics of an input signal, comprising: a level control unitwhich controls a level of the input signal; an automatic volume controlunit which automatically controls the volume of the input signal, thelevel of which is controlled by the level control unit; a characteristicadjustment unit which adjusts characteristics of the input signal, thevolume of which is automatically controlled by the automatic volumecontrol unit; and a coefficient calculation unit which calculates, fromthe input signal, a level control coefficient as a coefficient used toadjust control of the level control unit, an automatic volume controlcoefficient used to adjust control of the automatic volume control unit,and a characteristic adjustment coefficient used to adjust control ofthe characteristic adjustment unit.
 8. The apparatus according to claim7, wherein the coefficient calculation unit comprises: a clippingprevention coefficient calculation unit which calculates a clippingprevention coefficient used to prevent clipping and an appropriatevolume coefficient used to adjust to an appropriate volume from theinput signal; and an automatic volume control coefficient calculationunit which decides the automatic volume control coefficient based on thecalculated appropriate volume coefficient when a difference between theappropriate volume coefficient calculated by the clipping preventioncoefficient calculation unit and a previously adjusted automatic volumecontrol coefficient is less than a pre-set threshold, and decides theautomatic volume control coefficient based on the pre-set threshold whenthe difference between the calculated appropriate volume coefficient andthe previously adjusted automatic volume control coefficient, is greaterthan the pre-set threshold.
 9. The apparatus according to claim 8,wherein the clipping prevention coefficient calculation unit calculatesthe clipping prevention coefficient and the appropriate volumecoefficient based on feature quantities extracted from the input signaland an equation of square error minimum reference, which is prepared byexecuting, in advance, regression analysis of the feature quantities.10. A signal characteristic adjustment method, which adjustscharacteristics of an input signal, comprising steps of: controlling alevel of the input signal; adjusting characteristics of the inputsignal, the level of which is controlled in the step of controlling alevel; and calculating a level control coefficient used to adjust levelcontrol of the step of controlling a level and a characteristicadjustment coefficient used to adjust characteristic control of the stepof adjusting characteristics based on a feature quantity of the inputsignal.
 11. The method according to claim 10, wherein in the step ofcalculating a level control coefficient, clipping prevention coefficientused to prevent clipping is calculated based on the feature quantity ofthe input signal, the level control coefficient is decided based on theclipping prevention coefficient when the calculated clipping preventioncoefficient is greater than a pre-set threshold, and the level controlcoefficient is decided based on the pre-set threshold when thecalculated clipping prevention coefficient is less than the pre-setthreshold.
 12. The method according to claim 10, wherein in the step ofcalculating a level control coefficient, a clipping preventioncoefficient used to prevent clipping is calculated based on the featurequantity of the input signal, the characteristic adjustment coefficientis decided based on a previously adjusted characteristic adjustmentcoefficient when the calculated clipping prevention coefficient isgreater than a pre-set threshold, and the characteristic adjustmentcoefficient is decided based on a difference between the pre-setthreshold and the clipping prevention coefficient when the calculatedclipping prevention coefficient is less than the pre-set threshold. 13.The method according to claim 11, wherein in the step of calculating alevel control coefficient, the clipping prevention coefficient used toprevent clipping is calculated based on the feature quantity of theinput signal, the characteristic adjustment coefficient is decided basedon a previously adjusted characteristic adjustment coefficient when thecalculated clipping prevention coefficient is greater than the pre-setthreshold, and the characteristic adjustment coefficient is decidedbased on a difference between the pre-set threshold and the clippingprevention coefficient when the calculated clipping preventioncoefficient is less than the pre-set threshold.
 14. The method accordingto claim 11, wherein in the step of calculating a level controlcoefficient, the clipping prevention coefficient is calculated based onfeature quantities extracted from the input signal and an equation ofsquare error minimum reference, which is prepared by executing, inadvance, regression analysis of the feature quantities.
 15. The methodaccording to claim 11, wherein the step of calculating a level controlcoefficient comprises a step of calculating the clipping preventioncoefficient based on feature quantities of the input signal, featurequantities of the input signal, the characteristics of which areadjusted in the step of adjusting characteristics, and an equation ofsquare error minimum reference, which is prepared by executing, inadvance, regression analysis of the feature quantities.